• Yang T and Hasan R. Discovering Privacy Harms from Education Technology by Analyzing User Reviews. Proceedings of the 23rd Workshop on Privacy in the Electronic Society. (186-192).

    https://doi.org/10.1145/3689943.3695050

  • Rathod M, Yadav A, Phutane S and Bhise S. (2024). Comment Compass: An Approach to Analyze YouTube Comments through Web Scraping, Sentiment Analysis, and Generative AI for Actionable Insights 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT). 10.1109/IC2SDT62152.2024.10696483. 979-8-3503-6501-6. (1-5).

    https://ieeexplore.ieee.org/document/10696483/

  • Bindhumol M, Singh T and Patra P. (2024). Sentiment Analysis using YouTube Comments 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). 10.1109/ICCCNT61001.2024.10724166. 979-8-3503-7024-9. (1-7).

    https://ieeexplore.ieee.org/document/10724166/

  • Miyazaki K, Uchiba T, Kwak H, An J and Sasahara K. (2024). The impact of toxic trolling comments on anti-vaccine YouTube videos. Scientific Reports. 10.1038/s41598-024-54925-w. 14:1.

    https://www.nature.com/articles/s41598-024-54925-w

  • KAMA S. (2024). Meditation as a Leisure Activity: A Content and Comment Level AnalysisBoş Zaman Etkinliği Olarak Meditasyon: İçerik ve Yorum Düzeyi Analizi. GSI Journals Serie A: Advancements in Tourism Recreation and Sports Sciences. 10.53353/atrss.1412002.

    https://dergipark.org.tr/en/doi/10.53353/atrss.1412002

  • Zhu K, Khern-am-nuai W and Yu Y. (2024). Negative Peer Feedback and User Content Generation: Evidence From a Restaurant Review Platform. Production and Operations Management. 10.1177/10591478231224941.

    http://journals.sagepub.com/doi/10.1177/10591478231224941

  • Möller A, Vermeer S and Baumgartner S. (2023). Cutting Through the Comment Chaos: A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments. Social Science Computer Review. 10.1177/08944393231173895. 42:1. (162-185). Online publication date: 1-Feb-2024.

    http://journals.sagepub.com/doi/10.1177/08944393231173895

  • Meghana K. (2024). Artificial Intelligence and Sentiment Analysis in YouTube Comments: A Comprehensive Overview 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). 10.1109/IDCIoT59759.2024.10467782. 979-8-3503-2753-3. (1565-1572).

    https://ieeexplore.ieee.org/document/10467782/

  • Shetty M and Shetty S. (2024). Sentiment Analysis of Public Opinion Towards Reverse Diabetic Videos. International Conference on Innovative Computing and Communications. 10.1007/978-981-99-4071-4_7. (81-86).

    https://link.springer.com/10.1007/978-981-99-4071-4_7

  • ÇILGIN C. (2023). Emotion Analysis on Youtube Comments for 2023 Turkish Presidential Elections2023 Türkiye Cumhurbaşkanlığı Seçimleri için Youtube Yorumlarında Duygu Analizi. Yeni Medya Dergisi. 10.55609/yenimedya.1339272.

    https://dergipark.org.tr/en/doi/10.55609/yenimedya.1339272

  • Ahuja H, Kaur N, Kumar P and Hafiz A. (2023). Machine Learning based Sentiment Analysis of YouTube Video Comments 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI). 10.1109/ICAEECI58247.2023.10370907. 979-8-3503-4279-6. (1-6).

    https://ieeexplore.ieee.org/document/10370907/

  • A. S, S. S, P. R, D. A and M. S. (2023). Quality enhanced hybrid youtube video recommendation based on user preference through sentiment analysis on comments – a study on natural remedy videos. Multimedia Tools and Applications. 10.1007/s11042-023-17391-6. 83:15. (44217-44250).

    https://link.springer.com/10.1007/s11042-023-17391-6

  • Alafwan B, Siallagan M and Putro U. (2023). Comments Analysis on Social Media: A Review. ICST Transactions on Scalable Information Systems. 10.4108/eetsis.3843.

    https://publications.eai.eu/index.php/sis/article/view/3843

  • Pelttari S. (2022). YouTube. Internet Pragmatics. 10.1075/ip.00085.pel. 6:1. (42-66). Online publication date: 9-May-2023.

    https://benjamins.com/catalog/ip.00085.pel

  • Saito N, Zhang P, Hayashi H, Sugano S and Mori K. (2023). Innovation by Connecting People, Skill, and Value: A Community Platform for Collaborative Job Hunting 2023 IEEE 15th International Symposium on Autonomous Decentralized System (ISADS). 10.1109/ISADS56919.2023.10092110. 978-1-6654-6451-2. (1-6).

    https://ieeexplore.ieee.org/document/10092110/

  • Samiotis I, Mauri A, Lofi C and Bozzon A. (2023). On the Popularity of Classical Music Composers on Community-Driven Platforms. Web Engineering. 10.1007/978-3-031-34444-2_24. (327-335).

    https://link.springer.com/10.1007/978-3-031-34444-2_24

  • Ratajczyk D. (2022). Shape of the Uncanny Valley and Emotional Attitudes Toward Robots Assessed by an Analysis of YouTube Comments. International Journal of Social Robotics. 10.1007/s12369-022-00905-x. 14:8. (1787-1803). Online publication date: 1-Oct-2022.

    https://link.springer.com/10.1007/s12369-022-00905-x

  • Ray A, Bala P, Rana N and Dwivedi Y. (2022). Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers. Aslib Journal of Information Management. 10.1108/AJIM-12-2021-0357. 74:6. (1126-1150). Online publication date: 29-Sep-2022.

    https://www.emerald.com/insight/content/doi/10.1108/AJIM-12-2021-0357/full/html

  • Sun P, Wu L, Zhang K, Su Y and Wang M. (2021). An Unsupervised Aspect-Aware Recommendation Model with Explanation Text Generation. ACM Transactions on Information Systems. 40:3. (1-29). Online publication date: 31-Jul-2022.

    https://doi.org/10.1145/3483611

  • Alsafrjalani M. (2022). A Framework Model for Integrating Social Media, the Web, and Proprietary Services Into YouTube Video Classification Process. Research Anthology on Applying Social Networking Strategies to Classrooms and Libraries. 10.4018/978-1-6684-7123-4.ch015. (260-277).

    https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-7123-4.ch015

  • Hirano R, Okada R and Nakanishi T. (2022). Extraction Method for Important Words as a Viewer’s Reaction Arousal Factor from YouTube - Transcription 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI). 10.1109/IIAIAAI55812.2022.00129. 978-1-6654-9755-8. (651-652).

    https://ieeexplore.ieee.org/document/9894605/

  • Khatua A and Nejdl W. Endorsement Analysis of Migrant-related Deliberations on YouTube: Prior to and During 2022 Ukrainian crisis. Proceedings of the 2022 Workshop on Open Challenges in Online Social Networks. (31-38).

    https://doi.org/10.1145/3524010.3539499

  • Wang N, Clowdus Z, Sealander A and Stern R. (2022). Geonews: timely geoscience educational YouTube videos about recent geologic events. Geoscience Communication. 10.5194/gc-5-125-2022. 5:2. (125-142).

    https://gc.copernicus.org/articles/5/125/2022/

  • Niu S, Manon H, Bartolome A, Ha N and Veazey K. Close-up and Whispering: An Understanding of Multimodal and Parasocial Interactions in YouTube ASMR videos. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. (1-18).

    https://doi.org/10.1145/3491102.3517563

  • Huh M, Lee Y, Choi D, Kim H, Oh U and Kim J. Cocomix: Utilizing Comments to Improve Non-Visual Webtoon Accessibility. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. (1-18).

    https://doi.org/10.1145/3491102.3502081

  • Alaoui K and Pilotti M. (2022). The shifting grounds of humour: The case of Masameer in Saudi Arabia . Journal of Arab & Muslim Media Research. 10.1386/jammr_00043_1. 15:1. (107-124). Online publication date: 1-Apr-2022.

    https://www.ingentaconnect.com/content/10.1386/jammr_00043_1

  • Li F, Lu C, Lu Z, Carrington P and Truong K. (2022). An Exploration of Captioning Practices and Challenges of Individual Content Creators on YouTube for People with Hearing Impairments. Proceedings of the ACM on Human-Computer Interaction. 6:CSCW1. (1-26). Online publication date: 30-Mar-2022.

    https://doi.org/10.1145/3512922

  • Stappen L, Baird A, Lienhart M, Bätz A and Schuller B. (2022). An Estimation of Online Video User Engagement From Features of Time- and Value-Continuous, Dimensional Emotions. Frontiers in Computer Science. 10.3389/fcomp.2022.773154. 4.

    https://www.frontiersin.org/articles/10.3389/fcomp.2022.773154/full

  • Sui W, Sui A and Rhodes R. (2022). What to watch: Practical considerations and strategies for using YouTube for research. DIGITAL HEALTH. 10.1177/20552076221123707. 8. (205520762211237). Online publication date: 1-Jan-2022.

    http://journals.sagepub.com/doi/10.1177/20552076221123707

  • Dutta A, Sinha V, Chatterjee P, Debnath N and Sen S. (2022). A Multidimensional Data Mining Approach for Video Analysis and Ranking System. Advances in Data Science and Management. 10.1007/978-981-16-5685-9_59. (603-612).

    https://link.springer.com/10.1007/978-981-16-5685-9_59

  • Sorce G and Renz L. (2022). Exkludierend feministisch, solidarisch rassistisch: Die „120 Dezibel“-Kampagne auf YouTube. Exkludierende Solidarität der Rechten. 10.1007/978-3-658-36891-3_7. (133-150).

    https://link.springer.com/10.1007/978-3-658-36891-3_7

  • Salem H and Mazzara M. (2022). A NLP Framework to Generate Video from Positive Comments in Youtube. Advanced Information Networking and Applications. 10.1007/978-3-030-99619-2_19. (193-198).

    https://link.springer.com/10.1007/978-3-030-99619-2_19

  • Kurdi M, Albadi N and Mishra S. (2021). “Think before you upload”: an in-depth analysis of unavailable videos on YouTube. Social Network Analysis and Mining. 10.1007/s13278-021-00755-x. 11:1. Online publication date: 1-Dec-2021.

    https://link.springer.com/10.1007/s13278-021-00755-x

  • Pradhan R. (2021). Extracting Sentiments from YouTube Comments 2021 Sixth International Conference on Image Information Processing (ICIIP). 10.1109/ICIIP53038.2021.9702561. 978-1-6654-3361-7. (1-4).

    https://ieeexplore.ieee.org/document/9702561/

  • Boschi G, Young A, Joglekar S, Cammarota C and Sastry N. (2021). Who Has the Last Word? Understanding How to Sample Online Discussions. ACM Transactions on the Web. 15:3. (1-25). Online publication date: 31-Aug-2021.

    https://doi.org/10.1145/3452936

  • Dubovi I and Tabak I. (2021). Interactions between emotional and cognitive engagement with science on YouTube. Public Understanding of Science. 10.1177/0963662521990848. 30:6. (759-776). Online publication date: 1-Aug-2021.

    http://journals.sagepub.com/doi/10.1177/0963662521990848

  • Чуб А. (2021). VERBAL AND NONVERBAL MEANS OF IMPLEMENTING THE STRATEGY OF DISTRUST IN THE COMMENTS OF YOUTUBE USERS (BY THE MATERIAL OF THE COMMENTS TO VIDEOS ABOUT THE “DIE CORONA-WARN-APP”). Tomsk state pedagogical university bulletin. 10.23951/1609-624X-2021-4-67-74:4(216). (67-74). Online publication date: 6-Jul-2021.

    http://vestnik.tspu.edu.ru/archive.html?year=2021&issue=4&article_id=8162

  • Asani E, Vahdat-Nejad H and Sadri J. (2021). Restaurant recommender system based on sentiment analysis. Machine Learning with Applications. 10.1016/j.mlwa.2021.100114. (100114). Online publication date: 1-Jul-2021.

    https://linkinghub.elsevier.com/retrieve/pii/S2666827021000578

  • Zubbir N, Dass L and Ahmad N. (2021). Analysis on Adjective Suki and Its Co-occurrences in Japanese Youtube’s Comment. Pertanika Journal of Social Sciences and Humanities. 10.47836/pjssh.29.2.32. 29:2.

    http://www.pertanika.upm.edu.my/pjssh/browse/regular-issue?article=JSSH-7769-2020

  • Thomas S, Yuliana and Noviyanti. P . (2021). Study Analisis Metode Analisis Sentimen pada YouTube. Journal of Information Technology. 10.46229/jifotech.v1i1.201. 1:1. (1-7).

    https://journal.shantibhuana.ac.id/index.php/jifotech/article/view/201

  • de Guzman A, Mesana J and Roman J. (2021). Beyond the tip of the iceberg : a qualitative sentiment analysis of YouTube viewers’ emotional valence toward older adults auditioning in worldwide Got Talent® . Educational Gerontology. 10.1080/03601277.2021.1881868. (1-18).

    https://www.tandfonline.com/doi/full/10.1080/03601277.2021.1881868

  • Bender S and Broderick M. (2021). Virtual Reality, Trauma and Empathy. Virtual Realities. 10.1007/978-3-030-82547-8_5. (109-169).

    https://link.springer.com/10.1007/978-3-030-82547-8_5

  • Lim K and Lee C. (2021). Sharing is Learning: Using Topic Modelling to Understand Online Comments Shared by Learners. HCI International 2021 - Posters. 10.1007/978-3-030-78645-8_12. (91-101).

    https://link.springer.com/10.1007/978-3-030-78645-8_12

  • Gajanayake G and Sandanayake T. (2020). Trending Pattern Identification of YouTube Gaming Channels Using Sentiment Analysis 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer). 10.1109/ICTer51097.2020.9325476. 978-1-7281-8655-9. (149-154).

    https://ieeexplore.ieee.org/document/9325476/

  • Young A, Joglekar S, Boschi G and Sastry N. Ranking comment sorting policies in online debates. Argument & Computation. 10.3233/AAC-200909. (1-21).

    https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/AAC-200909

  • Hong S and Pittman M. (2020). eWOM anatomy of online product reviews: interaction effects of review number, valence, and star ratings on perceived credibility. International Journal of Advertising. 10.1080/02650487.2019.1703386. 39:7. (892-920). Online publication date: 2-Oct-2020.

    https://www.tandfonline.com/doi/full/10.1080/02650487.2019.1703386

  • Teng S, Khong K, Pahlevan Sharif S and Ahmed A. (2020). YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis. JMIR Public Health and Surveillance. 10.2196/19618. 6:4. (e19618).

    https://publichealth.jmir.org/2020/4/e19618

  • Eke R, Li T, Bond K, Ho A and Graves L. (2020). Viewing Trends and Users’ Perceptions of the Effect of Sleep-Aiding Music on YouTube: Quantification and Thematic Content Analysis. Journal of Medical Internet Research. 10.2196/15697. 22:8. (e15697).

    http://www.jmir.org/2020/8/e15697/

  • Singh Chauhan G, Kumar Meena Y, Gopalani D and Nahta R. (2020). A two-step hybrid unsupervised model with attention mechanism for aspect extraction. Expert Systems with Applications. 10.1016/j.eswa.2020.113673. (113673). Online publication date: 1-Jun-2020.

    https://linkinghub.elsevier.com/retrieve/pii/S0957417420304978

  • Peng Z, Guo Q, Tsang K and Ma X. Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. (1-15).

    https://doi.org/10.1145/3313831.3376695

  • Ishii A and Kawahata Y. (2020). Theory of Opinion Distribution in Human Relations Where Trust and Distrust Mixed. Intelligent Decision Technologies. 10.1007/978-981-15-5925-9_40. (471-478).

    http://link.springer.com/10.1007/978-981-15-5925-9_40

  • Kawahata Y. (2020). Examination of Analysis Method of Opinion Distribution in News Media Transferred on Web. Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems. 10.1007/978-3-030-37442-6_14. (156-166).

    http://link.springer.com/10.1007/978-3-030-37442-6_14

  • Sai Nikhita N, Hyndavi V and Trupthi M. (2020). Detection of Inappropriate Anonymous Comments Using NLP and Sentiment Analysis. Advances in Decision Sciences, Image Processing, Security and Computer Vision. 10.1007/978-3-030-24322-7_17. (131-138).

    https://link.springer.com/10.1007/978-3-030-24322-7_17

  • Dabas C, Kaur P, Gulati N and Tilak M. (2019). Analysis of Comments on Youtube Videos using Hadoop 2019 Fifth International Conference on Image Information Processing (ICIIP). 10.1109/ICIIP47207.2019.8985907. 978-1-7281-0899-5. (353-358).

    https://ieeexplore.ieee.org/document/8985907/

  • Ishii A and Kawahata Y. New opinion dynamics theory considering interpersonal relationship of both trust and distrust. IEEE/WIC/ACM International Conference on Web Intelligence - Companion Volume. (43-50).

    https://doi.org/10.1145/3358695.3360927

  • Tur-Viñes V and Castelló-Martínez A. (2019). Commenting on Top Spanish YouTubers: “No Comment”. Social Sciences. 10.3390/socsci8100266. 8:10. (266).

    https://www.mdpi.com/2076-0760/8/10/266

  • Yarmand M, Yoon D, Dodson S, Roll I and Fels S. "Can you believe [1:21]?!". Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. (1-12).

    https://doi.org/10.1145/3290605.3300719

  • Talton J, Dusad K, Koiliaris K and Kumar R. How do People Sort by Ratings?. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. (1-10).

    https://doi.org/10.1145/3290605.3300535

  • Baowaly M, Tu Y and Chen K. Predicting the helpfulness of game reviews: a case study on the Steam store. Journal of Intelligent & Fuzzy Systems. 10.3233/JIFS-179022. (1-12).

    https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-179022

  • Alsafrjalani M. (2019). A Framework Model for Integrating Social Media, the Web, and Proprietary Services Into YouTube Video Classification Process. International Journal of Multimedia Data Engineering and Management. 10.4018/IJMDEM.2019040102. 10:2. (21-36). Online publication date: 1-Apr-2019.

    https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2019040102

  • Yahav I, Shehory O and Schwartz D. (2019). Comments Mining With TF-IDF. IEEE Transactions on Knowledge and Data Engineering. 31:3. (437-450). Online publication date: 1-Mar-2019.

    https://doi.org/10.1109/TKDE.2018.2840127

  • Alsafrjalani M. (2019). An Extensible, Modular Framework for Classifying YouTube Videos Using Web and Social Media 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). 10.1109/MIPR.2019.00092. 978-1-7281-1198-8. (459-462).

    https://ieeexplore.ieee.org/document/8695377/

  • Chauhan G and Meena Y. (2019). YouTube Video Ranking by Aspect-Based Sentiment Analysis on User Feedback. Immunological Tolerance. 10.1007/978-981-13-3600-3_6. (63-71).

    http://link.springer.com/10.1007/978-981-13-3600-3_6

  • Ng Y and Jung U. (2019). Personalized Book Recommendation Based on a Deep Learning Model and Metadata. Web Information Systems Engineering – WISE 2019. 10.1007/978-3-030-34223-4_11. (162-178).

    http://link.springer.com/10.1007/978-3-030-34223-4_11

  • Chang W. (2018). Will Sentiments in Comments Influence Online Video Popularity? 2018 IEEE International Conference on Big Data (Big Data). 10.1109/BigData.2018.8621938. 978-1-5386-5035-6. (3644-3646).

    https://ieeexplore.ieee.org/document/8621938/

  • Zhang J, Danescu-Niculescu-Mizil C, Sauper C and Taylor S. (2018). Characterizing Online Public Discussions through Patterns of Participant Interactions. Proceedings of the ACM on Human-Computer Interaction. 2:CSCW. (1-27). Online publication date: 1-Nov-2018.

    https://doi.org/10.1145/3274467

  • Yu B, Kelly R and Watts L. Reacting to Political Videos. Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing. (141-144).

    https://doi.org/10.1145/3272973.3274040

  • Vlachos E and Tan Z. (2018). Public perception of android robots: Indications from an analysis of YouTube comments 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 10.1109/IROS.2018.8594058. 978-1-5386-8094-0. (1255-1260).

    https://ieeexplore.ieee.org/document/8594058/

  • Hussain M, Tokdemir S, Agarwal N and Al-Khateeb S. (2018). Analyzing Disinformation and Crowd Manipulation Tactics on YouTube 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 10.1109/ASONAM.2018.8508766. 978-1-5386-6051-5. (1092-1095).

    https://ieeexplore.ieee.org/document/8508766/

  • Lee J and Nerghes A. (2018). Refugee or Migrant Crisis? Labels, Perceived Agency, and Sentiment Polarity in Online Discussions. Social Media + Society. 10.1177/2056305118785638. 4:3. Online publication date: 1-Jul-2018.

    https://journals.sagepub.com/doi/10.1177/2056305118785638

  • Möller A, Kühne R, Baumgartner S and Peter J. (2018). Exploring User Responses to Entertainment and Political Videos. Social Science Computer Review. 10.1177/0894439318779336. (089443931877933).

    http://journals.sagepub.com/doi/10.1177/0894439318779336

  • Blagus N and Zitnik S. (2018). Social media comparison and analysis: The best data source for research? 2018 12th International Conference on Research Challenges in Information Science (RCIS). 10.1109/RCIS.2018.8406662. 978-1-5386-6517-6. (1-10).

    https://ieeexplore.ieee.org/document/8406662/

  • Lu Z, Xia H, Heo S and Wigdor D. You Watch, You Give, and You Engage. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. (1-13).

    https://doi.org/10.1145/3173574.3174040

  • Ghosh A, Badillo-Urquiola K, Guha S, LaViola Jr J and Wisniewski P. Safety vs. Surveillance. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. (1-14).

    https://doi.org/10.1145/3173574.3173698

  • Jiao Y, Li C, Wu F and Mei Q. Find the Conversation Killers. Proceedings of the 2018 World Wide Web Conference. (1145-1154).

    https://doi.org/10.1145/3178876.3186013

  • Abdullah A, Ali M, Karabatak M and Sengur A. (2018). A comparative analysis of common YouTube comment spam filtering techniques 2018 6th International Symposium on Digital Forensic and Security (ISDFS). 10.1109/ISDFS.2018.8355315. 978-1-5386-3449-3. (1-5).

    https://ieeexplore.ieee.org/document/8355315/

  • Thelwall M and Mas-Bleda A. (2018). YouTube science channel video presenters and comments: female friendly or vestiges of sexism?. Aslib Journal of Information Management. 10.1108/AJIM-09-2017-0204. 70:1. (28-46). Online publication date: 15-Jan-2018.

    https://www.emerald.com/insight/content/doi/10.1108/AJIM-09-2017-0204/full/html

  • Tan E, Seaman I and Ng Y. (2018). Using Online Metadata to Enhance Religious Video Search. Data Science Analytics and Applications. 10.1007/978-981-10-8603-8_16. (188-203).

    http://link.springer.com/10.1007/978-981-10-8603-8_16

  • Daniel C, Mullarkey M and Hevner A. (2018). Capturing User Generated Video Content in Online Social Networks. Designing for a Digital and Globalized World. 10.1007/978-3-319-91800-6_22. (333-347).

    http://link.springer.com/10.1007/978-3-319-91800-6_22

  • Wang G, Gu W and Suh A. (2018). The Effects of 360-Degree VR Videos on Audience Engagement: Evidence from the New York Times. HCI in Business, Government, and Organizations. 10.1007/978-3-319-91716-0_17. (217-235).

    http://link.springer.com/10.1007/978-3-319-91716-0_17

  • Barnes R. (2018). The Online/Offline Life. Uncovering Online Commenting Culture. 10.1007/978-3-319-70235-3_3. (47-65).

    http://link.springer.com/10.1007/978-3-319-70235-3_3

  • Abbas M, Riaz M, Rauf A, Khan M and Khalid S. (2017). Context-aware Youtube recommender system 2017 International Conference on Information and Communication Technologies (ICICT). 10.1109/ICICT.2017.8320183. 978-1-5386-2186-8. (161-164).

    http://ieeexplore.ieee.org/document/8320183/

  • Yao Y, Tong H, Xu F and Lu J. (2017). On the Measurement and Prediction of Web Content Utility. ACM SIGKDD Explorations Newsletter. 19:2. (1-12). Online publication date: 21-Nov-2017.

    https://doi.org/10.1145/3166054.3166056

  • Ng Y and Jin M. Personalized Recipe Recommendations for Toddlers Based on Nutrient Intake and Food Preferences. Proceedings of the 9th International Conference on Management of Digital EcoSystems. (243-250).

    https://doi.org/10.1145/3167020.3167057

  • Rinaldi E and Musdholifah A. (2017). FVEC-SVM for opinion mining on Indonesian comments of youtube video 2017 International Conference on Data and Software Engineering (ICoDSE). 10.1109/ICODSE.2017.8285860. 978-1-5386-1449-5. (1-5).

    http://ieeexplore.ieee.org/document/8285860/

  • Al-Tamimi A, Shatnawi A and Bani-Issa E. (2017). Arabic sentiment analysis of YouTube comments 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). 10.1109/AEECT.2017.8257766. 978-1-5090-5969-0. (1-6).

    http://ieeexplore.ieee.org/document/8257766/

  • Bhuiyan H, Ara J, Bardhan R and Islam M. (2017). Retrieving YouTube video by sentiment analysis on user comment 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). 10.1109/ICSIPA.2017.8120658. 978-1-5090-5559-3. (474-478).

    http://ieeexplore.ieee.org/document/8120658/

  • McClanahan B and Gokhale S. (2017). Interplay between video recommendations, categories, and popularity on YouTube 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). 10.1109/UIC-ATC.2017.8397661. 978-1-5386-0435-9. (1-7).

    https://ieeexplore.ieee.org/document/8397661/

  • Penni J. (2017). The future of online social networks (OSN). Telematics and Informatics. 34:5. (498-517). Online publication date: 1-Aug-2017.

    https://doi.org/10.1016/j.tele.2016.10.009

  • Gauba H, Kumar P, Roy P, Singh P, Dogra D and Raman B. (2017). Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Networks. 10.1016/j.neunet.2017.01.013. 92. (77-88). Online publication date: 1-Aug-2017.

    https://linkinghub.elsevier.com/retrieve/pii/S0893608017300345

  • Lee J and Nerghes A. Labels and sentiment in social media. Proceedings of the 8th International Conference on Social Media & Society. (1-10).

    https://doi.org/10.1145/3097286.3097300

  • Zhang C, Liu J, Ma M, Sun L and Li B. Seeker. Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video. (25-30).

    https://doi.org/10.1145/3083165.3083179

  • Momeni E, Rawassizadeh R and Adar E. Leveraging Semantic Facets for Adaptive Ranking of Social Comments. Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. (356-364).

    https://doi.org/10.1145/3078971.3079004

  • Poché E, Jha N, Williams G, Staten J, Vesper M and Mahmoud A. Analyzing user comments on YouTube coding tutorial videos. Proceedings of the 25th International Conference on Program Comprehension. (196-206).

    https://doi.org/10.1109/ICPC.2017.26

  • Sriraghav K, Jayanthi S, Vidya N and Enigo V. (2017). ScrAnViz — A tool to scrap, analyze and visualize unstructured-data using attribute-based opinion mining algorithm 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). 10.1109/IPACT.2017.8244916. 978-1-5090-5682-8. (1-5).

    http://ieeexplore.ieee.org/document/8244916/

  • Gilbert E, Lampe C, Leavitt A, Lo K and Yarosh L. Conceptualizing, Creating, & Controlling Constructive and Controversial Comments. Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. (425-430).

    https://doi.org/10.1145/3022198.3022663

  • Malik H and Tian Z. (2017). A Framework for Collecting YouTube Meta-Data. Procedia Computer Science. 10.1016/j.procs.2017.08.347. 113. (194-201).

    https://linkinghub.elsevier.com/retrieve/pii/S187705091731757X

  • Khan M. (2017). Social media engagement. Computers in Human Behavior. 66:C. (236-247). Online publication date: 1-Jan-2017.

    https://doi.org/10.1016/j.chb.2016.09.024

  • Liao C, Squicciarini A, Griffin C and Rajtmajer S. (2016). A hybrid epidemic model for deindividuation and antinormative behavior in online social networks. Social Network Analysis and Mining. 10.1007/s13278-016-0321-5. 6:1. Online publication date: 1-Dec-2016.

    http://link.springer.com/10.1007/s13278-016-0321-5

  • Li H, Cui J, Shen B and Ma J. (2016). An intelligent movie recommendation system through group-level sentiment analysis in microblogs. Neurocomputing. 210:C. (164-173). Online publication date: 19-Oct-2016.

    https://doi.org/10.1016/j.neucom.2015.09.134

  • Pham D, Le A and Le T. (2016). Learning Semantic Representations for Rating Vietnamese Comments 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE). 10.1109/KSE.2016.7758052. 978-1-4673-8929-7. (193-198).

    http://ieeexplore.ieee.org/document/7758052/

  • Rangaswamy S, Ghosh S, Jha S and Ramalingam S. (2016). Metadata extraction and classification of YouTube videos using sentiment analysis 2016 International Carnahan Conference on Security Technology (ICCST). 10.1109/CCST.2016.7815692. 978-1-5090-1072-1. (1-2).

    http://ieeexplore.ieee.org/document/7815692/

  • Wendt L, Griesbaum J and Kölle R. (2016). (2016). Product advertising and viral stealth marketing in online videos. Aslib Journal of Information Management. 10.1108/AJIM-11-2015-0174. 68:3. (250-264). Online publication date: 16-May-2016.. Online publication date: 16-May-2016.

    https://www.emerald.com/insight/content/doi/10.1108/AJIM-11-2015-0174/full/html

  • Tang J, Venolia G and Inkpen K. Meerkat and Periscope. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. (4770-4780).

    https://doi.org/10.1145/2858036.2858374

  • Tyson G, Elkhatib Y, Sastry N and Uhlig S. (2016). Measurements and Analysis of a Major Adult Video Portal. ACM Transactions on Multimedia Computing, Communications, and Applications. 12:2. (1-25). Online publication date: 3-Mar-2016.

    https://doi.org/10.1145/2854003

  • Ksiazek T, Peer L and Lessard K. (2014). User engagement with online news: Conceptualizing interactivity and exploring the relationship between online news videos and user comments. New Media & Society. 10.1177/1461444814545073. 18:3. (502-520). Online publication date: 1-Mar-2016.

    https://journals.sagepub.com/doi/10.1177/1461444814545073

  • Garrett N. (2016). Mapping Self-Guided Learners’ Searches for Video Tutorials on YouTube. Journal of Educational Technology Systems. 10.1177/0047239515615851. 44:3. (319-331). Online publication date: 1-Mar-2016.

    https://journals.sagepub.com/doi/10.1177/0047239515615851

  • Momeni E, Cardie C and Diakopoulos N. (2015). A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web. ACM Computing Surveys. 48:3. (1-49). Online publication date: 8-Feb-2016.

    https://doi.org/10.1145/2811282

  • Balakrishnan V, Ahmadi K and Ravana S. (2016). (2015). Improving retrieval relevance using users’ explicit feedback. Aslib Journal of Information Management. 10.1108/AJIM-07-2015-0106. 68:1. (76-98). Online publication date: 18-Jan-2016.. Online publication date: 31-Dec-2016.

    https://www.emerald.com/insight/content/doi/10.1108/AJIM-07-2015-0106/full/html

  • Severyn A, Moschitti A, Uryupina O, Plank B and Filippova K. (2016). Multi-lingual opinion mining on YouTube. Information Processing and Management: an International Journal. 52:1. (46-60). Online publication date: 1-Jan-2016.

    https://doi.org/10.1016/j.ipm.2015.03.002

  • Chen J, Zhang C and Niu Z. (2016). Identifying Helpful Online Reviews with Word Embedding Features. Knowledge Science, Engineering and Management. 10.1007/978-3-319-47650-6_10. (123-133).

    http://link.springer.com/10.1007/978-3-319-47650-6_10

  • Abolkasim E, Lau L and Dimitrova V. (2016). A Semantic-Driven Model for Ranking Digital Learning Objects Based on Diversity in the User Comments. Adaptive and Adaptable Learning. 10.1007/978-3-319-45153-4_1. (3-15).

    http://link.springer.com/10.1007/978-3-319-45153-4_1

  • Teh P, Rayson P, Pak I and Piao S. Sentiment analysis tools should take account of the number of exclamation marks!!!. Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services. (1-6).

    https://doi.org/10.1145/2837185.2837216

  • You Q, Cao L, Cong Y, Zhang X and Luo J. A Multifaceted Approach to Social Multimedia-Based Prediction of Elections. IEEE Transactions on Multimedia. 10.1109/TMM.2015.2487863. 17:12. (2271-2280).

    http://ieeexplore.ieee.org/document/7293668/

  • Song M, Jeong Y and Kim H. (2015). Identifying the topology of the K-pop video community on YouTube. Journal of the Association for Information Science and Technology. 66:12. (2580-2595). Online publication date: 1-Dec-2015.

    https://doi.org/10.1002/asi.23346

  • Miller E. (2015). Content Analysis of Select YouTube Postings: Comparisons of Reactions to the Sandy Hook and Aurora Shootings and Hurricane Sandy. Cyberpsychology, Behavior, and Social Networking. 10.1089/cyber.2015.0045. 18:11. (635-640). Online publication date: 1-Nov-2015.

    http://www.liebertpub.com/doi/10.1089/cyber.2015.0045

  • Chung J. (2015). Antismoking campaign videos on YouTube and audience response. Computers in Human Behavior. 51:PA. (114-121). Online publication date: 1-Oct-2015.

    https://doi.org/10.1016/j.chb.2015.04.061

  • Kofler C, Bhattacharya S, Larson M, Chen T, Hanjalic A and Chang S. Uploader Intent for Online Video: Typology, Inference, and Applications. IEEE Transactions on Multimedia. 10.1109/TMM.2015.2445573. 17:8. (1200-1212).

    http://ieeexplore.ieee.org/document/7123627/

  • Chen Y, Chen T, Liu T, Liao H and Chang S. Assistive Image Comment Robot—A Novel Mid-Level Concept-Based Representation. IEEE Transactions on Affective Computing. 10.1109/TAFFC.2014.2388370. 6:3. (298-311).

    http://ieeexplore.ieee.org/document/7001614/

  • Sun J, Wang G, Cheng X and Fu Y. (2015). Mining affective text to improve social media item recommendation. Information Processing and Management: an International Journal. 51:4. (444-457). Online publication date: 1-Jul-2015.

    https://doi.org/10.1016/j.ipm.2014.09.002

  • Wang J, Yan Z, Yang L and Huang B. (2015). An approach to rank reviews by fusing and mining opinions based on review pertinence. Information Fusion. 23:C. (3-15). Online publication date: 1-May-2015.

    https://doi.org/10.1016/j.inffus.2014.04.002

  • Xu J, van der Schaar M, Liu J and Li H. (2015). Timely video popularity forecasting based on social networks IEEE INFOCOM 2015 - IEEE Conference on Computer Communications. 10.1109/INFOCOM.2015.7218618. 978-1-4799-8381-0. (2308-2316).

    http://ieeexplore.ieee.org/document/7218618/

  • Xu J, van der Schaar M, Liu J and Li H. Forecasting Popularity of Videos Using Social Media. IEEE Journal of Selected Topics in Signal Processing. 10.1109/JSTSP.2014.2370942. 9:2. (330-343).

    http://ieeexplore.ieee.org/document/6955832/

  • Hu X and Liu H. (2015). Social Media, Mining and Profiling in. The International Encyclopedia of Digital Communication and Society. 10.1002/9781118767771.wbiedcs126. (1-6).

    https://onlinelibrary.wiley.com/doi/10.1002/9781118767771.wbiedcs126

  • Leaning M. (2015). Mumsnet Zombies: Surviving the Zombie Apocalypse on Mumsnet and YouTube. The Zombie Renaissance in Popular Culture. 10.1057/9781137276506_10. (141-159).

    http://link.springer.com/10.1057/9781137276506_10

  • Orimaye S, Alhashmi S and Siew E. (2013). Performance and trends in recent opinion retrieval techniques. The Knowledge Engineering Review. 10.1017/S0269888913000167. 30:1. (76-105). Online publication date: 1-Jan-2015.

    https://www.cambridge.org/core/product/identifier/S0269888913000167/type/journal_article

  • Khurshid S, Khan S and Bashir S. (2014). Text-Based Intelligent Content Filtering on Social Platforms 2014 12th International Conference on Frontiers of Information Technology (FIT). 10.1109/FIT.2014.51. 978-1-4799-7505-1. (232-237).

    http://ieeexplore.ieee.org/document/7118405/

  • Bakhshi S, Kanuparthy P and Shamma D. If It Is Funny, It Is Mean. Proceedings of the 2014 ACM International Conference on Supporting Group Work. (46-52).

    https://doi.org/10.1145/2660398.2660414

  • Wu Y, Kita K and Matsumoto K. (2014). Three predictions are better than one: Sentence multi‐emotion analysis from different perspectives. IEEJ Transactions on Electrical and Electronic Engineering. 10.1002/tee.22020. 9:6. (642-649). Online publication date: 1-Nov-2014.

    https://onlinelibrary.wiley.com/doi/10.1002/tee.22020

  • Vasconcelos M, Almeida J, Gonçalves M, Souza D and Gomes G. Popularity dynamics of foursquare micro-reviews. Proceedings of the second ACM conference on Online social networks. (119-130).

    https://doi.org/10.1145/2660460.2660484

  • Chelaru S, Herder E, Naini K and Siehndel P. Recognizing skill networks and their specific communication and connection practices. Proceedings of the 25th ACM conference on Hypertext and social media. (13-23).

    https://doi.org/10.1145/2631775.2631801

  • Radulescu C, Dinsoreanu M and Potolea R. (2014). Identification of spam comments using natural language processing techniques 2014 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). 10.1109/ICCP.2014.6936976. 978-1-4799-6569-4. (29-35).

    http://ieeexplore.ieee.org/document/6936976/

  • Chelaru S, Orellana-Rodriguez C and Altingovde I. (2014). How useful is social feedback for learning to rank YouTube videos?. World Wide Web. 17:5. (997-1025). Online publication date: 1-Sep-2014.

    https://doi.org/10.1007/s11280-013-0258-9

  • Zhang Y and Zhang D. (2014). Automatically predicting the helpfulness of online reviews 2014 IEEE International Conference on Information Reuse and Integration (IRI). 10.1109/IRI.2014.7051953. 978-1-4799-5880-1. (662-668).

    http://ieeexplore.ieee.org/document/7051953/

  • Jawaheer G, Weller P and Kostkova P. (2014). Modeling User Preferences in Recommender Systems. ACM Transactions on Interactive Intelligent Systems. 4:2. (1-26). Online publication date: 1-Jul-2014.

    https://doi.org/10.1145/2512208

  • Rowe M and Alani H. Mining and comparing engagement dynamics across multiple social media platforms. Proceedings of the 2014 ACM conference on Web science. (229-238).

    https://doi.org/10.1145/2615569.2615677

  • Bonsón E, Bednarova M and Escobar-Rodríguez T. (2014). (2014). Corporate YouTube practices of Eurozone companies. Online Information Review. 10.1108/OIR-07-2013-0181. 38:4. (484-501). Online publication date: 12-Jun-2014.. Online publication date: 12-Jun-2014.

    https://www.emerald.com/insight/content/doi/10.1108/OIR-07-2013-0181/full/html

  • Siersdorfer S, Chelaru S, Pedro J, Altingovde I and Nejdl W. (2014). Analyzing and Mining Comments and Comment Ratings on the Social Web. ACM Transactions on the Web. 8:3. (1-39). Online publication date: 1-Jun-2014.

    https://doi.org/10.1145/2628441

  • Park J, Jang J, Jaimes A, Chung C and Myaeng S. Exploring the user-generated content (UGC) uploading behavior on youtube. Proceedings of the 23rd International Conference on World Wide Web. (529-534).

    https://doi.org/10.1145/2567948.2576945

  • Sipos R, Ghosh A and Joachims T. Was this review helpful to you?. Proceedings of the 23rd international conference on World wide web. (337-348).

    https://doi.org/10.1145/2566486.2567998

  • Chen Y, Chen T, Hsu W, Liao H and Chang S. Predicting Viewer Affective Comments Based on Image Content in Social Media. Proceedings of International Conference on Multimedia Retrieval. (233-240).

    https://doi.org/10.1145/2578726.2578756

  • Ramamonjisoa D. (2014). Topic modeling on users's comments 2014 Third ICT International Student Project Conference (ICT-ISPC). 10.1109/ICT-ISPC.2014.6923245. 978-1-4799-5573-2. (177-180).

    http://ieeexplore.ieee.org/document/6923245/

  • Hobel H, Schrittwieser S, Kieseberg P and Weippl E. (2014). Anonymity and Pseudonymity in Data-Driven Science. Encyclopedia of Business Analytics and Optimization. 10.4018/978-1-4666-5202-6.ch013. (124-130).

    http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-4666-5202-6.ch013

  • Sang J. (2014). User-Perceptive Multimedia Content Analysis. User-centric Social Multimedia Computing. 10.1007/978-3-662-44671-3_2. (11-32).

    https://link.springer.com/10.1007/978-3-662-44671-3_2

  • Chowdhury S and Makaroff D. (2014). Category-Based YouTube Request Pattern Characterization. Web Information Systems and Technologies. 10.1007/978-3-662-44300-2_10. (154-169).

    https://link.springer.com/10.1007/978-3-662-44300-2_10

  • Kieseberg P, Hobel H, Schrittwieser S, Weippl E and Holzinger A. (2014). Protecting Anonymity in Data-Driven Biomedical Science. Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. 10.1007/978-3-662-43968-5_17. (301-316).

    http://link.springer.com/10.1007/978-3-662-43968-5_17

  • Zhang Z, Wei Q and Chen G. (2014). Estimating Online Review Helpfulness with Probabilistic Distribution and Confidence. Foundations and Applications of Intelligent Systems. 10.1007/978-3-642-37829-4_35. (411-420).

    https://link.springer.com/10.1007/978-3-642-37829-4_35

  • Krishna A, Zambreno J and Krishnan S. Polarity trend analysis of public sentiment on YouTube. Proceedings of the 19th International Conference on Management of Data. (125-128).

    /doi/10.5555/2694476.2694505

  • Chelaru S, Altingovde I, Siersdorfer S and Nejdl W. (2013). Analyzing, Detecting, and Exploiting Sentiment in Web Queries. ACM Transactions on the Web. 8:1. (1-28). Online publication date: 1-Dec-2013.

    https://doi.org/10.1145/2535525

  • Zhu J, Luo J, You Q and Smith J. (2013). Towards Understanding the Effectiveness of Election Related Images in Social Media 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). 10.1109/ICDMW.2013.112. 978-1-4799-3142-2. (421-425).

    http://ieeexplore.ieee.org/document/6753951/

  • Orimaye S, Alhashmi S and Siew E. (2013). Can predicate-argument structures be used for contextual opinion retrieval from blogs?. World Wide Web. 16:5-6. (763-791). Online publication date: 1-Nov-2013.

    https://doi.org/10.1007/s11280-012-0170-8

  • Li H, Ma X, Wang F, Liu J and Xu K. On popularity prediction of videos shared in online social networks. Proceedings of the 22nd ACM international conference on Information & Knowledge Management. (169-178).

    https://doi.org/10.1145/2505515.2505523

  • Rudinac S, Larson M and Hanjalic A. (2013). Learning Crowdsourced User Preferences for Visual Summarization of Image Collections. IEEE Transactions on Multimedia. 15:6. (1231-1243). Online publication date: 1-Oct-2013.

    https://doi.org/10.1109/TMM.2013.2261481

  • Madden A, Ruthven I and McMenemy D. (2013). (2013). A classification scheme for content analyses of YouTube video comments. Journal of Documentation. 10.1108/JD-06-2012-0078. 69:5. (693-714). Online publication date: 2-Sep-2013.. Online publication date: 2-Sep-2013.

    https://www.emerald.com/insight/content/doi/10.1108/JD-06-2012-0078/full/html

  • Courtois C, Mechant P, Ostyn V and De Marez L. (2013). Uploaders' definition of the networked public on YouTube and their feedback preferences: a multi-method approach. Behaviour & Information Technology. 10.1080/0144929X.2011.586727. 32:6. (612-624). Online publication date: 1-Jun-2013.

    http://www.tandfonline.com/doi/abs/10.1080/0144929X.2011.586727

  • Serbanoiu A and Rebedea T. Relevance-Based Ranking of Video Comments on YouTube. Proceedings of the 2013 19th International Conference on Control Systems and Computer Science. (225-231).

    https://doi.org/10.1109/CSCS.2013.87

  • Galvis Carreño L and Winbladh K. Analysis of user comments: an approach for software requirements evolution. Proceedings of the 2013 International Conference on Software Engineering. (582-591).

    /doi/10.5555/2486788.2486865

  • Trestian I, Xiao C and Kuzmanovic A. A glance at an overlooked part of the world wide web. Proceedings of the 22nd International Conference on World Wide Web. (1379-1386).

    https://doi.org/10.1145/2487788.2488178

  • Carreno L and Winbladh K. (2013). Analysis of user comments: An approach for software requirements evolution 2013 35th International Conference on Software Engineering (ICSE). 10.1109/ICSE.2013.6606604. 978-1-4673-3076-3. (582-591).

    http://ieeexplore.ieee.org/document/6606604/

  • Backstrom L, Kleinberg J, Lee L and Danescu-Niculescu-Mizil C. Characterizing and curating conversation threads. Proceedings of the sixth ACM international conference on Web search and data mining. (13-22).

    https://doi.org/10.1145/2433396.2433401

  • Chelaru S, Orellana-Rodriguez C and Altingovde I. Can social features help learning to rank youtube videos?. Proceedings of the 13th international conference on Web Information Systems Engineering. (552-566).

    https://doi.org/10.1007/978-3-642-35063-4_40

  • Chowdhury S and Makaroff D. Characterizing Videos and Users in YouTube. Proceedings of the 2012 Seventh International Conference on Broadband, Wireless Computing, Communication and Applications. (244-251).

    https://doi.org/10.1109/BWCCA.2012.47

  • Ma Z, Sun A, Yuan Q and Cong G. Topic-driven reader comments summarization. Proceedings of the 21st ACM international conference on Information and knowledge management. (265-274).

    https://doi.org/10.1145/2396761.2396798

  • Mahajan D, Rastogi R, Tiwari C and Mitra A. LogUCB. Proceedings of the 21st ACM international conference on Information and knowledge management. (6-15).

    https://doi.org/10.1145/2396761.2396767

  • Sayago S, Forbes P and Blat J. Older people's social sharing practices in YouTube through an ethnographical lens. Proceedings of the 26th Annual BCS Interaction Specialist Group Conference on People and Computers. (185-194).

    /doi/10.5555/2377916.2377937

  • Meyers E. (2012). A Comment on Learning: Media Literacy Practices in YouTube. International Journal of Learning and Media. 10.1162/IJLM_a_00100. 4:3-4. (33-47). Online publication date: 1-Jul-2012.

    http://www.portico.org/Portico/article?article=phx73mtdh2q

  • Sood S, Antin J and Churchill E. Profanity use in online communities. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. (1481-1490).

    https://doi.org/10.1145/2207676.2208610

  • Ammari A, Lau L and Dimitrova V. Deriving group profiles from social media to facilitate the design of simulated environments for learning. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. (198-207).

    https://doi.org/10.1145/2330601.2330650

  • Orimaye S, Alhashmi S and Eu-gene S. Sentiment analysis amidst ambiguities in youtube comments on yoruba language (nollywood) movies. Proceedings of the 21st International Conference on World Wide Web. (583-584).

    https://doi.org/10.1145/2187980.2188138

  • Shmueli E, Kagian A, Koren Y and Lempel R. Care to comment?. Proceedings of the 21st international conference on World Wide Web. (429-438).

    https://doi.org/10.1145/2187836.2187895

  • Dalal O, Sengemedu S and Sanyal S. Multi-objective ranking of comments on web. Proceedings of the 21st international conference on World Wide Web. (419-428).

    https://doi.org/10.1145/2187836.2187894

  • Mishra A and Rastogi R. Semi-supervised correction of biased comment ratings. Proceedings of the 21st international conference on World Wide Web. (181-190).

    https://doi.org/10.1145/2187836.2187862

  • Thelwall M, Sud P and Vis F. (2012). Commenting on YouTube videos: From guatemalan rock to El Big Bang. Journal of the American Society for Information Science and Technology. 63:3. (616-629). Online publication date: 1-Mar-2012.

    https://doi.org/10.1002/asi.21679

  • Moghaddam S, Jamali M and Ester M. ETF. Proceedings of the fifth ACM international conference on Web search and data mining. (163-172).

    https://doi.org/10.1145/2124295.2124316

  • Sood S, Churchill E and Antin J. (2012). Automatic identification of personal insults on social news sites. Journal of the American Society for Information Science and Technology. 63:2. (270-285). Online publication date: 1-Feb-2012.

    https://doi.org/10.1002/asi.21690

  • Orimaye S, Alhashmi S and Eu-Gene S. Semantic-based opinion retrieval using predicate-argument structures and subjective adjectives. Proceedings of the 7th Asia conference on Information Retrieval Technology. (372-385).

    https://doi.org/10.1007/978-3-642-25631-8_34

  • Orimaye S, Alhashmi S and Eu-Gene S. Using predicate-argument structures for context-dependent opinion retrieval. Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II. (386-402).

    https://doi.org/10.1007/978-3-642-25856-5_29

  • Deng S, Mitsubuchi T, Shioda K, Shimada T and Sakurai A. Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction. Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing. (800-807).

    https://doi.org/10.1109/DASC.2011.138

  • Ding Y, Du Y, Hu Y, Liu Z, Wang L, Ross K and Ghose A. Broadcast yourself. Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference. (361-370).

    https://doi.org/10.1145/2068816.2068850

  • De Moor K, De Pessemier T, Mechant P, Courtois C, Verdejo A, De Marez L and Martens L. (2011). Users' (Dis)satisfaction with the personalTV application. Computers in Entertainment. 9:3. (1-22). Online publication date: 1-Nov-2011.

    https://doi.org/10.1145/2027456.2027464

  • Fernandez-Luque L, Karlsen R and Melton G. HealthTrust. Proceedings of the 20th ACM international conference on Information and knowledge management. (1917-1920).

    https://doi.org/10.1145/2063576.2063854

  • Willemsen L, Neijens P, Bronner F and de Ridder J. (2011). “Highly Recommended!” The Content Characteristics and Perceived Usefulness of Online Consumer Reviews. Journal of Computer-Mediated Communication. 10.1111/j.1083-6101.2011.01551.x. 17:1. (19-38). Online publication date: 1-Oct-2011.

    https://academic.oup.com/jcmc/article/17/1/19-38/4067647

  • Rowe M, Angeletou S and Alani H. (2011). Anticipating Discussion Activity on Community Forums 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust (PASSAT) / 2011 IEEE Third Int'l Conference on Social Computing (SocialCom). 10.1109/PASSAT/SocialCom.2011.215. 978-1-4577-1931-8. (315-322).

    http://ieeexplore.ieee.org/document/6113130/

  • Kamath K and Caverlee J. (2011). Expert-Driven Topical Classification of Short Message Streams 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust (PASSAT) / 2011 IEEE Third Int'l Conference on Social Computing (SocialCom). 10.1109/PASSAT/SocialCom.2011.213. 978-1-4577-1931-8. (388-393).

    http://ieeexplore.ieee.org/document/6113139/

  • Ammari A, Dimitrova V and Despotakis D. Identifying relevant youtube comments to derive socially augmented user models. Proceedings of the 19th international conference on Advances in User Modeling. (71-85).

    https://doi.org/10.1007/978-3-642-28509-7_8

  • Lee Y, Kim E, Cho H and Woo G. Visualizing dispute sections and relations from the sequence of replying comments. Proceedings of the 2011 ACM Symposium on Applied Computing. (786-791).

    https://doi.org/10.1145/1982185.1982355

  • Asselin M, Dobson T, Meyers E, Teixiera C and Ham L. Learning from YouTube. Proceedings of the 2011 iConference. (640-642).

    https://doi.org/10.1145/1940761.1940851

  • Zhu K, Khern-am-nuai W and Yu Y. Any Feedback is Welcome: Peer Feedback and User Behavior on Digital Platforms. SSRN Electronic Journal. 10.2139/ssrn.4009873.

    https://www.ssrn.com/abstract=4009873

  • Liu X, Zhang B, Susarla A and Padman R. Go to YouTube and See Me Tomorrow: Social Media and Self-Care of Chronic Conditions. SSRN Electronic Journal. 10.2139/ssrn.3061149.

    https://www.ssrn.com/abstract=3061149

  • Kaprans M. Did We Miss the Social Commentary? Responding to Borat on Youtube. SSRN Electronic Journal. 10.2139/ssrn.1749719.

    http://www.ssrn.com/abstract=1749719